Abstract
Recent years have witnessed the great successes of embedding-based methods in recommender systems. Despite their decent performance, we argue one potential limitation of these methods — the embedding magnitude has not been explicitly modulated, which may aggravate popularity bias and training instability, hindering the model from making a good recommendation. It motivates us to leverage the embedding normalization in recommendation. By normalizing user/item embeddings to a specific value, we empirically observe impressive performance gains (9% on average) on four real-world datasets. Although encouraging, we also reveal a serious limitation when applying normalization in recommendation — the performance is highly sensitive to the choice of the temperature $\tau$ which controls the scale of the normalized embeddings. To fully foster the merits of the normalization while circumvent its limitation, this work studied on how to adaptively set the proper $\tau$. Towards this end, we first make a comprehensive analyses of $\tau$ to fully understand its role on recommendation. We then accordingly develop an adaptive fine-grained strategy Adap-$\tau$ for the temperature with satisfying four desirable properties including adaptivity, personalized, efficiency and model-agnostic. Extensive experiments have been conducted to validate the effectiveness of the proposal. The code is available at https://github.com/junkangwu/Adap_tau.
Citation:
@inproceedings{chen2023adap,
title={Adap-$\tau$: Adaptively Modulating Embedding Magnitude for Recommendation},
author={Chen, Jiawei and Wu, Junkang and Wu, Jiancan and Cao, Xuezhi and Zhou, Sheng and He, Xiangnan},
booktitle={Proceedings of the ACM Web Conference 2023},
pages={1085--1096},
year={2023}
}
Junkang Wu
吴俊康
Jiancan Wu
吴剑灿 博士后
